Current and Future Tropical Cyclone Projects at CIRA/NESDIS: An Update and Outlook

1. Current and Future Tropical Cyclone Projects at CIRA/NESDIS: An Update and Outlook Presented by John Knaff CIRA National Hurricane Center

2. Acknowledgments • Nick Shea, Michelle Manelli, Jim Kossin, John Kaplan, Julie Demuth, Ray Zehr, Mark DeMaria, and many others…. • A special thanks to Kristina Katsaros for paying my way! National Hurricane Center

3. Outline • JHT • AMSU • SHIPS • Insurance Friends • Ensemble SHIPS • Wind Probablities • Continuing and Future Projects National Hurricane Center

4. Tropical Cyclone Intensity and Size Estimation From AMSU Data • CIRA algorithm • Physical retrieval of T, P and wind from all AMSU-A channels • Statistical estimation of max wind, min pressure, radii of 34, 50 and 64 kt winds using input from retrievals National Hurricane Center

5. AMSU Instrument Properties • AMSU-A1 • 13 frequencies 50-89 GHz • 48 km maximum resolution • Vertical temperature profiles 0-45 km • AMSU-A2 • 2 frequencies 23.8, 31.4 GHz • 48 km maximum resolution • Precipitable water, cloud water, rain rate NOAA – 15, Launched May 13, 1998 National Hurricane Center

6. Horizontal Resolution: AMSU-A vs. MSU Useful Region National Hurricane Center

7. Vertical Weighting: AMSU-A National Hurricane Center

8. Typical AMSU Data Coverage (NOAA-15) Total Precipitable Water National Hurricane Center

9. Hurricane Temperature Profile National Hurricane Center

10. Apply a statistical retrieval to obtain vertical temperature (T) profiles and integrated cloud liquid water (CLW) from all available AMSU limb-corrected brightness temperatures at the satellite footprint locations. The storm must be within 600 km of the swath center for the algorithm to run. • Apply statistical correction to T in regions of high CLW to account for attenuation. • Interpolate T and CLW to an evenly spaced lat/lon grid using a two-pass Barnes analysis • Apply “Laplacian” filter to remove localized cold anomalies resulting from ice scattering • Using a smoothness upper boundary condition, integrate the hydrostatic equation downwards from 50 hPa to the surface to obtain the pressure field • Assume gradient balance to obtain the tangential wind from the pressure field as a function of radius and height • Statistically relate parameters from the retrieved CLW, temperature, pressure and wind fields to the observed MSLP National Hurricane Center

11. Ta Ta Without Correction With Correction V V National Hurricane Center

12. AMSU Surface Pressure for Floyd 14 Sept 1999 Uncorrected Corrected National Hurricane Center

13. Statistically relate parameters from the retrieved CLW, temperature, pressure and wind fields to the observed MSLP and Wind National Hurricane Center

14. National Hurricane Center

15. Statistical Coefficients/ Wind, MSLP National Hurricane Center

16. Performance Tests National Hurricane Center

17. 2002 Verification through 11 Oct • What was produced in real-time at CIRA • Most got to NHC • 243 Cases National Hurricane Center

18. Bias = -1.9 mb MAE = 7.6 mb (6.7) RMSE = 10.28 mb (9.3) National Hurricane Center

19. Skeleton Storms National Hurricane Center

20. Bias = 3.0 kt MAE = 10.9 kt (11.0) RMSE = 14.1 kt (14.1) National Hurricane Center

21. Bad AMSU Good Dvorak Good Skeleton Storms National Hurricane Center

22. AMSU Wind Radii Estimates • Predict azimuthally averaged winds with multiple linear regression • Apply asymmetries based upon motion. National Hurricane Center

23. Estimation of Wind Radii • 20 potential predictors are used to predict the mean radii of 35, 50, and 65 knot winds if they exist. • Asymmetries are accounted for by a simple relationship • Actual mean radii are estimated using But are solved using a variational method since all radii rarely exist.

24. Cost Function and Variational Analysis • Cost Function (thing to minimize iteratively) Last two terms are used to constrain the results close to climatology (penalty terms) – climatological r and x are functions of intensity.

25. Coefficients/ Wind Radii National Hurricane Center

26. Performance Tests for Symmetric Winds National Hurricane Center

27. National Hurricane Center

28. Performance Tests for Asymmetric Winds National Hurricane Center

29. Verification • Will be done following the season with g-deck information. National Hurricane Center

30. Future Plans for AMSU Analyses • Finish program to access data from BUFR files • Dostalek/Krautkramer collaboration • Perform validation of 2002 season • Investigate method to remove high bias for “skeleton” storms • Generalize wind model to improve asymmetric wind radii estimation • Current method only includes wavenumber one asymmetry due to motion • Obtain higher-order asymmetries from AMSU nonlinear balance winds National Hurricane Center

31. Balance Equation Variational Solution - Hurricane Floyd AMSU 850 hPa height First guess wind: (Ñ2u=0, Ñ2v=0) Nonlinear balance wind National Hurricane Center

32. SHIPS Improvements Using Satellite Data(NESDIS/CIRA JHT Project) • Parallel version of SHIPS with predictors from GOES and satellite altimetry data • % Pixel counts < -20 C • BT standard deviation • Ocean Heat Content along storm track > 50 kJ/m2 • Provides correction to operational SHIPS forecast (No land and decay version of SHIPS) • Implemented on NCEP/IBM Aug 20, 2002 beginning with Dolly National Hurricane Center

33. Sample Image and Radial/Time Profiles for Hurricane Floyd 1999 Mean Tb Tb std dev Pixels < -30 C Pixels < -40 C Pixels < -50 C Pixels < -60 C National Hurricane Center

34. SAMPLE OCEANIC HEAT CONTENT (26 Sept 2002) National Hurricane Center

35. Preliminary 2002 Intensity Validation • Arthur-Lili as of 11 Oct 2002 • NHC validation rules with working best track • 186 cases at 12 hr, 77 cases at 120 hr • Conclusion: It was a difficult year for intensity forecasting National Hurricane Center

36. Impact of GOES/OHC Predictors on Decay-SHIPS Forecasts • Dolly-Lili as of 11 Oct 2002 • NHC validation rules with working best track • 169 cases at 12 hr, 78 cases at 120 hr • Positive Impact 0-60 hr, Negative impact 72-120 hr • Negative impact influenced by Isidore track forecast errors National Hurricane Center

37. Separation of GOES and OHC Effects • GOES data provide modest corrections (~0-8 kt) for most storms • OHC has little effect for most storms, but large increases (up to 20 kt) over limited regions • Impacts of GOES and OHC will be evaluated separately after the season • Preliminary analysis: • OHC effects: Isidore, Lili • GOES effects: Dolly, Edouard, Fay, Gustav, Hanna, Josephine, Kyle National Hurricane Center

38. Impact of GOES Predictors on Decay-SHIPS Forecasts • Sample for cases without OHC impacts • Dolly-Kyle without Isidore • Positive Impact 0-108 hr, Negative impact 120 hr • GOES predictors are independent of track forecast • GOES data reduced intensity of sheared cases • (2002 sample ideal for this effect) National Hurricane Center

39. Future Improvements to SHIPS • Short Term • Evaluate SST and OHC with smaller time step • Update SST analysis daily (already done by C. Sisko) • Test GOES predictors in east Pacific SHIPS • Longer Term (Possible new JHT project) • Neural Network Prediction • Time step approach • (0-12, 12-24, … instead of 0-12, 0-24, etc) • Extend developmental sample back to 1981 using re-analysis data • Investigate “False Start” tropical cyclones • Develop specialized prediction for hurricanes • Account for inner core structure (eye wall cycles, etc) • Incorporate aircraft data, possibly microwave imagery National Hurricane Center

40. “False Start” Tropical Cyclones • Significant intensification predicted by SHIPS, but storm decays in low-shear environment with warm SSTs • Danny 1991, Cindy, Bret 1993, TD05 1997, Alex 1998, Ernesto, Joyce 2000, Chantal, Erin 2001, Dolly 2002 • Large source of SHIPS (and NHC forecast errors • Develop objective measure for “False Starts” • Examine storm environment to aid identification • Emphasis on thermodynamics • Possible connection with Saharan Air Layer National Hurricane Center

41. D-SHIPS forecasts for Chantal 2001 and Dolly 2002 (“False Start” Storms) National Hurricane Center

42. SHIPS Ensemble Forecasts(“Insurance Friends” Project) • Provide a range of plausible forecast tracks using error characteristics of NHC official forecasts • Errors fitted to analytic distributions • Errors sampled at 0.5 and 1.0 • Run ensemble SHIPS and Decay-SHIPS • Calculate ensemble mean and ensemble spread • Preliminary results for Dolly through Lili • Includes depression stage • Validated using working best track • 239 cases at 12 hr, 121 at 120 hr National Hurricane Center

43. Sample 16-member Track Ensemble(East Pacific case) National Hurricane Center

44. Control and Ensemble Mean SHIPS Errors, and Average Ensemble Spread (No land correction) National Hurricane Center

45. Control and Ensemble Mean D-SHIPS Errors, and Average Ensemble Spread (With land correction) National Hurricane Center

46. Sample N-AWIPS Ensemble Product(Developed by M. Mainelli) National Hurricane Center

47. Preliminary Conclusions from SHIPS Ensembles • SHIPS ensembles of limited utility over the ocean due to small forecast spread • More useful for storms near land masses • 3-5 day forecast improvements of ~10% • Analysis will be performed using final best track as well as the parallel versions of SHIPS at the end of the 2002 season • Results will be presented at the IHC National Hurricane Center

48. Monte Carlo Wind Speed Probabilities(New “Insurance Friends” Project) • Determine ensemble of forecast tracks using probability distributions from NHC official track forecast errors • Generalization of method used for tracks for SHIPS ensembles • For each track, determine intensity forecast by sampling from NHC intensity error distribution • Adjustment for movement over land using empirical decay equation from SHIPS • For ensemble members with high enough wind speeds, estimate wind radii (34, 50 and 64 kt) from observed sample • Calculate field of probabilities for various wind thresholds from ensemble track/radii forecasts • Plans for Atlantic prototype by June 2003 National Hurricane Center

49. Future Directions • Expand the tropical cyclone genesis parameter to a multibasin, basin-wide application. • Track probability density functions derived from Jonathan Vigh’s Kilo-ensemble track forecasts • Satellite based tropical cyclone surface wind analysis National Hurricane Center